Mehdi Bohlouli and Sadegh Alijani

Abstract

In this research the existence of genotype by environment interaction (G ×
E)on milk production traits (milk yield, fat yield, protein yield, fat
percentage and protein percentage) was investigated by considering relevant
records in different production levels as different traits. Production level
(PL) considered as the average milk production traits of herd-year
calving.Data on Holstein cows from 2001 to 2010 were used. Herd-years of
calving means for milk production traits were clustered in three levels
using the FASTCLUS procedure in SAS software. Production levels included
low, medium and high levels. For these traits, G × E were investigated by
applying a multiple-trait random regression sire model.

Additive genetic and permanent environmental variances of milk production traits varied in
different production levels. Estimated heritabilities for milk production
traits as a function of days in milk were highest in high production levels
with an exception for protein percentage. Generally, the highest
heritability estimates of 305-d milk production traitswere found in high PL
rather than low PL. Low spearman correlations between estimated breeding
values of the 20 top sires among low and medium PL (0.38) and between low
and high PL (0.39) of milk yield showed re-ranking of sires for these
levels.The greatest G×E was observed for milk yield and protein percentage,
with a genetic correlation for 305-d equal to 0.79 between low and high
production levels. Results from this research indicated that milk production
of daughters of the same sires depends greatly on the production
environment.

Introduction

The environments in which dairy farming is practiced in Iran vary in many
ways, such as the average herd production, level of feeding, elevation and
climate variables including temperature and humidity. The phenomenon by
which different genotypes respond differently to changes in their
environments is known as genotype by environment interaction (G×E) or
as differences in environmental sensitivity of genotypes (Falconer and
Mackay 1996). This interaction can cause different ranking of animals across
environments or a change of scale, i.e., variance, across environments
(Lynch and Walsh 1998). If a genotype by environment interaction exists, the
ranking of sires for milk production traits of their daughters will vary
from one environment to another. However, re-ranking of sires across
environments is limited for milk production traits (Veerkamp et al 1995;
Cromie et al 1998; Calus et al 2002); although there is evidence that
variances and heritabilities vary. An environmental parameter reflected the
environment encountered by the animals; such as production level of herds (Veerkamp
and Goddard 1998; Calus et al 2002), or other characteristics of the herds,
such as average age at calving (Fikse et al 2003).

Typically, estimated genetic correlations across environments are used to
estimate the degree of re-ranking. Genotype by environmental interaction is
reflected by the genetic correlation between different production levels.
High estimates of genetic correlations between environments (>0.80) suggest
no evidence for strong G × E (Robertson 1959). Using herd production level
as a substitute for the level of feeding, Veerkamp and Goddard (1998)
reported a genetic correlation of 0.79 between Australian dairy herds with
<20 kg of milk yield per day and herd with >24 kg of milk production per
day.

Calus et al(2002) reported similar results for genetic correlations between
extreme classes of herd production level in Dutch dairy cattle.Hammami et al
(2008) reported that genetic correlation for 305-days milk between Tunisia
and Luxemburg countries was 0.60 and spearman rank correlation between
estimated breeding values (EBV) of common sires for this trait from
within-country analyses was 0.41. Kolver et al
(2002) reported significant G×E for milk and fertility traits when the
performance of New Zealand and imported Holstein Friesian dairy cows were
compared on all pasture or total mixed ration systems.The purpose of the
present research was to estimate genotype by environment interaction for
milk production traits and to investigate the effects of production levels
on re-ranking of sires EBVs.

Material and methods

Data

Total
of test-day records for first lactation Holstein cows from 2001 to 2010 were
extracted from the Animal Breeding Center database at Karaj, Iran. Records
were edited on the following criteria: cows with known sires and having age
at first calving from 21 to 46 month, edits excluded irregular data for
daily milk yield (<1.0 and >75 kg), fat percentage (<1.5% and >9%), and
protein percentage (<1% and >7%), cows were required to have a minimum of
five test-day (TD) records between 5 and 305 days in milk (DIM) and
herd-year of calving subclasses with at last 10 cow records. Additional data
edits eliminated sires that had progeny in fewer than three herds and herds
that used fewer than three sires.

The
FASTCLUS procedure of SAS software (SAS 2003) was used to clustering and
average of trait in herd-year of calving grouped in three clusters.
Common sires had at last 10 daughters in each group of production levels.
The pedigree file included animal code, sire code and maternal grandsire code. Therefore, all of the sires had genetic relationship with common sires kept in the analysis.

Statistical analysis

For
examining a genotype × production levels interaction, the performances of
cows in production levels were considered as different traits. Thus, the
following multiple-trait sire model, which considered the performance of
daughters of sires in the three production levels as different correlated
traits, was fitted:

The following (co)variance structure was assumed for random effects of
model:

where G is sire genetic (co)variance matrix among random
regression coefficients and A is additive numerator
relationship matrix between sires. The matrix P was the cow
effects variance-covariance matrix among random regression coefficients, and
e was residual variances for each traits and I
represents an identity matrix with ones on the diagonal. G and
P are 12×12 (co)variances matrix of regression coefficients.
All across-production levels (co)variances in P equal to zero
because this effect was considered independent across production levels.

The first four Legendre polynomial functions (Kirkpatric et al 1990) were
given as:

where
w is a standardized unit of DIM and ranged from -1 to +1. Estimated (co)variance
component of milk production traits were obtained by REMLF90 program based
on restricted maximum likelihood method (Misztal et al 2002).

Calculation of genetic parameters

The
sire and permanent environmental (co)variances matrices for each DIM were
calculated as:

In sire model analysis, EBVs were computed via multiplying sire additive
genetic value by 2. Spearman rank correlations between EBV of common sires in
each group were used to assess the level of re-ranking of sires in different
production levels for milk yield, protein yield, fat yield, protein
percentage and fat percentage calculated using the procedure CORR of SAS
(SAS2003).

Results and discussion

Descriptive statistics

The mean, standard deviation and coefficient of variation of milk production
traits and other descriptive statistics for different production levels are
summarized in Table 1. The number of cows per common sires was the lowest in
the low production level and highest in high production level for milk
yield. For other traits, the medium production level has the highest cows
number per common sires.For milk yield, the average age in the low
production group was larger than in the other levels. Coefficient of
determinations by production levels for each trait (using PROCFASTCLUS) were
higher than 0.70.PROC FASTCLUS caused that within group’s variances
decreased to minimum, and maximized differences between groups;
therefore, coefficient of variance in each group was low (Table 1).

Table 1. Descriptive statistics of data sets
for milk production traits in different production levels (PL)

PL

Parameter

Low

Medium

High

Total

Milk yield

TD records, no.

87431

261269

478598

827295

Means ±SD (kg)

19.9 ±2.42

26.7 ±1.54

31.2 ±1.56

27.5 ±4.08

CV (%)

12.1

5.76

5.00

14.8

Cows, no.

10666

30854

56616

98136

Common sire, no.

373

373

373

373

Cows. /common sire, no.

28.6

82.7

151.8

263

HY, no.

420

951

775

2146

HTD, no.

4782

10214

8003

22999

Age at first calving (avg), mo.

30.2

26.9

26.6

27.0

Fat yield

TD records, no.

146561

415377

265114

827052

Means ±SD (g)

683±94.7

911±61.4

1126±90.3

929±173

CV (%)

13.9

6.74

8.02

18.7

Cows, no.

17916

53410

33807

105133

Common sire, no.

366

366

366

366

Cows. /common sire, no.

48.9

145

92.4

287

HY, no.

444

1104

658

2206

HTD, no.

4847

12025

7781

24653

Age at first calving (avg), mo.

27.5

26.9

26.5

26.9

Protein yield

TD records, no.

46016

326381

217933

590330

Means ±SD (g)

663±102

863±48.5

1023±71.3

891±135

CV (%)

15.4

5.62

6.97

15.2

Cows, no.

6845

42802

28325

77972

Common sire, no.

324

324

324

324

Cows. /common sire, no.

21.1

132

87.4

240

HY, no.

263

950

589

1802

HTD, no.

2199

9524

6637

18360

Age at first calving (avg), mo.

27.9

26.8

26.6

26.8

Fat %

TD records, no.

199931

376226

136896

713053

Means ±SD (%)

3.04 ±0.137

3.36 ±0.0831

3.67 ±0.131

3.41 ±0.248

CV (%)

4.51

2.47

3.57

7.27

Cows, no.

24328

46853

16747

87928

Common sire, no.

405

405

405

405

Cows /common sire, no.

60.1

115

41.4

217

HY, no.

643

1237

515

2395

HTD, no.

6225

12131

5066

23422

Age at first calving (avg), mo.

26.8

27.0

27.0

26.9

Protein %

TD records, no.

165047

357267

130133

652446

Means ±SD (%)

2.92 ±0.0950

3.11 ±0.0549

3.36 ±0.133

3.07 ±0.142

CV (%)

3.25

1.77

3.96

4.63

Cows, no.

21319

43911

16009

81239

Common sire, no.

430

430

430

430

Cows. /common sire, no.

49.6

102

37.2

188

HY, no.

403

1162

151

1716

HTD, no.

4173

12333

1648

18154

Age at first calving (avg), mo.

26.3

26.8

27.0

26.2

Variance components and heritabilities

The
sire additive genetic, permanent environmental and residual variances for
305-day production were heterogeneous among production levels, but the sire
additive genetic variances of medium and high levels for protein percentage
were similar and lower than sire additive variances in low production level
(Table 2).

Table
3 reflected (co)variance components for random regression coefficients for
calculating of sire additive genetic, permanent environmental and
residual variances (
respectively) of each trait in different PL. Residual variances for all
traits in the low production level were lower than inother levels.
Difference in variance component values between PL indicated that there were
differences in heritabilities between PL. Heritabilities for milk production
traits as a function of DIM in the three levels of production are shown in
Figure 1. Heritabilities for milk yield by DIM were higher than for other
traits. These heritabilities are in accordance with other studies (Shadparvar
and Yazdanshenas 2005; Abdullahpour et al 2010). Generally, heritabilities
for milk production traits were the highest in the high production level,
except for protein percentage. The highest heritabilities for protein
percentage in low PL was due to highest sire additive genetic variances.

Figure 1a.
Estimated heritability (h2) as a function of days in
milk (DIM) in three levels of production for milk yield

Figure 1b.
Estimated heritability (h2) as a function of days in
milk (DIM) in three levels of production for fat yield

Figure 1c.
Estimated heritability (h2) as a function of days in
milk (DIM) in three levels of production for protein yield

Figure 1d.
Estimated heritability (h2) as a function of days in
milk (DIM) in three levels of production for fat percentage

Figure 1e.
Estimated heritability (h2) as a function of days in
milk (DIM) in three levels of production for protein percentage

Generally, heritability for 305-days production in low PL was lower than in high
PL. But for protein percentage, heritability in the low production level was
higher than for the other levels (Table 4). Greater heritability values for
herds with greater milk yield averages have been observed frequently. Veerkamp
and Goddard (1998) indicated that the genetic variance for milk yield in
high-input systems is greater than the genetic variances in low-input systems.
Van Vleck (1988) indicated that genetic and phenotypic variances were different
from farm to farm, in most cases. The reason of this difference is the result of
a more complete expression of the genetic potential in the high production level
as result of a better environment (Hill et al 1983; Powell et al 1983 and Ceron-Munoz
et al 2004).

Different genetic variances and heritabilities in different production levels
revealed unequal genetic expression of dairy cattle genes. Differences in
additive genetic variances obtained for different production levels imply that a
scaling effect exists for EBV of sires across these PL.

The genetic correlation coefficients for milk production traits between PL
varied and indicated the presence of a G×E interaction. Generally, the lowest
genetic correlations were estimated across low and high production levels that
were 0.79 for milk yield and protein percentage, suggesting that G×E would have
an important impact on animal performance (Robertson 1959). For fat percentage,
genetic correlations between production levels were greater than 0.90,
suggesting that sires will rank similarly in the three production levels (Table
4). However, differences in variance estimates across these production levels
may lead to scaling effects in sires’ EBV, especially between low and high
levels. The low genetic correlations are thought to be due to differences in
feeding systems or feeding levels and climate variables between herds on
different PL.

Spearman
rank correlations across three production levels for 20 top sires, are given in
Table 4. Spearman rank correlations for milk yield across PL were lower than
other traits. These correlations between low and medium and also between low and
high production levels were less than 0.40, whereas between medium and high
production levels was 0.96. Correlation between EBV of sires in low and high PL
for protein percentage was 0.60. This results reflected low genetic correlation
between these PL (rg<0.80). Low genetic and spearman correlations are
translated as re-ranking of sires across production levels. Cienfuegos-Rivas et
al (1999) found low rank correlation coefficient (0.59) between herds in low
milk production level in Mexico and all herds in the United States. They
concluded that this result was evidence for a significant G × E interaction and
that sires were ranked differently in the Mexican environment compared with
their ranking in the United States. Peterson (1988) reported that re-ranking was
observed for Canadian sires when they were used in New Zealand. The authors
suspected this is caused by the decreased ability of Canadian sires daughters to
get sufficient energy intakes from exclusive pasture regimens in New Zealand.
When genetic correlations of traits decreased, the need for a separate breeding
program increases (Mulder and Bijma 2006; Nauta et al 2006).

Conclusion

In
general, results from this study indicated potential interactions between
genotype and environment for milk production traits in Iranian Holsteins dairy
cattle. One possible use of this information would be to improve the accuracy of
evaluation of performance of sires in different production levels. Therefore,
special sires should be selected for each production levels.

Acknowledgments

The authors thank animal breeding center of Karaj, Iran for providing the data.
We also want to express our deepest thanks to Hedi Hammami for his helpful
contribution.